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ORIGINAL RESEARCH article

Front. Oral. Health
Sec. Oral Health Promotion
Volume 5 - 2024 | doi: 10.3389/froh.2024.1427109
This article is part of the Research Topic Responsible Artificial Intelligence and Machine Learning Methods for Equity in Oral Health View all 3 articles

Comparing two large data repositories to understand the differences in demographics, health history, and behavioral attributes in populations

Provisionally accepted
Nihmath Nasiha Maliq Nihmath Nasiha Maliq Toan Ong Toan Ong Zachary Giano Zachary Giano William Rivera William Rivera Tamanna Tiwari Tamanna Tiwari *
  • University of Colorado Denver, Denver, United States

The final, formatted version of the article will be published soon.

    This study conducted a comparative analysis between two large data repositories, the All of Us (AoU) medical data and BigMouth dental data repositories. The comparison analysis includes variables related to behavioral and systemic health, health literacy, and overall health status across race, ethnicity, and gender. The analytic approach used descriptive statistics, Chi-square, odds ratio, and 95% confidence intervals; significant comparisons were measured with Cohen's D effect sizes. In the AoU dataset, 80.6% of Hispanic or Latino participants reported alcohol use compared to 16.8% in the BigMouth data repository. The female cohort in AoU showed 87.9% alcohol use, a contrast to BigMouth's 26.0%. Additionally, the diabetes prevalence among females was 8.8% in AoU versus 21.6% in BigMouth. Differences in health literacy were observed, with 49.2% among Hispanic or Latino participants in AoU, in contrast to BigMouth's 3.2%. Despite this, 70.1% of Hispanic or Latino respondents in AoU reported satisfactory health status, while BigMouth indicated a much higher figure at 98.3%. These variations highlight the importance of targeted health interventions addressing racial/ethnic and gender influences. Differences may arise from recruitment approaches, participant demographics, and healthcare access. There is a need for collaboration, standardized data collection, and inclusive recruitment to remedy these discrepancies. Further research is imperative to understand the underlying causes, facilitate interventions that address the disparities, and advocate for a more inclusive healthcare system.

    Keywords: Electronic Health Record, behavioral health, systemic health, Health Literacy, big data

    Received: 02 May 2024; Accepted: 14 Nov 2024.

    Copyright: © 2024 Nasiha Maliq, Ong, Giano, Rivera and Tiwari. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Tamanna Tiwari, University of Colorado Denver, Denver, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.